DESIGN AND EVALUATION OF IMAGE PROCESSING MODEL USING MACHINE LEARNING APPROACHES

Abstract

This research presents a comprehensive exploration of hybrid deep learning architectures designed to address class imbalance and generalization problems in image classification, focusing on breast cancer and brain tumor diagnosis while tackling challenges like data scarcity, imbalance, and feature inconsistency. Key contributions include novel hybrid architectures integrating CNNs, ViTs, and GANs, enhancing robustness and adaptability. A dual-modification approach combining data augmentation with algorithmic adjustments, such as optimized loss functions, effectively balanced class representation. Additionally, the incorporation of GANs and auxiliary neural networks for tumor classification demonstrates a substantial increase in diagnostic performance by generating diverse synthetic data and using auxiliary spatial features. The introduction of an Efficient Attention Mechanism and a Resource-Efficient Optimization model further refines breast cancer detection, providing high-dimensional feature integration that enhances diagnostic precision while reducing computational overhead. The proposed models demonstrated improved performance by effectively addressing class imbalance and generalization, achieving 9% to 10% gains in multi-class classification and 1% to 2% gains in binary classification. Additionally, the model complexity, in terms of time and space, was reduced by 2% to 4%, while scalability improved by 1% to 2% in binary tasks and 9% to 10% in multi class tasks, highlighting their efficiency and adaptability. Through the extensive evaluation this research establishes a robust, generalizable framework for image classification, with future implications for integrating multi-modal data and advancing interpretability in clinical settings.